Terraform Provider for CAST AI is a tool that allows users to manage their CAST AI resources using Terraform. It provides a seamless integration between Terraform and CAST AI platform, enabling users to define and manage their infrastructure as code. The provider supports various features such as setting up cluster configurations, managing node templates, and configuring autoscaler policies. Users can easily install the provider, pass API keys, and leverage the provider's functionalities to automate the deployment and management of their CAST AI resources.

Clawlet is an ultra-lightweight and efficient personal AI assistant that comes as a single binary with no CGO, runtime, or dependencies. It features hybrid semantic memory search and is inspired by OpenClaw and nanobot. Users can easily download Clawlet from GitHub Releases and drop it on any machine to enable memory search functionality. The tool supports various LLM providers like OpenAI, OpenRouter, Anthropic, Gemini, and local endpoints. Users can configure Clawlet for memory search setup and chat app integrations for platforms like Telegram, WhatsApp, Discord, and Slack. Clawlet CLI provides commands for initializing workspace, running the agent, managing channels, scheduling jobs, and more.

Crush is a versatile tool designed to enhance coding workflows in your terminal. It offers support for multiple LLMs, allows for flexible switching between models, and enables session-based work management. Crush is extensible through MCPs and works across various operating systems. It can be installed using package managers like Homebrew and NPM, or downloaded directly. Crush supports various APIs like Anthropic, OpenAI, Groq, and Google Gemini, and allows for customization through environment variables. The tool can be configured locally or globally, and supports LSPs for additional context. Crush also provides options for ignoring files, allowing tools, and configuring local models. It respects `.gitignore` files and offers logging capabilities for troubleshooting and debugging.

Easy, fast, and cheap pretrain, finetune, serving for everyone

Capsule is a secure and durable runtime for AI agents, designed to coordinate tasks in isolated environments. It allows for long-running workflows, large-scale processing, autonomous decision-making, and multi-agent systems. Tasks run in WebAssembly sandboxes with isolated execution, resource limits, automatic retries, and lifecycle tracking. It enables safe execution of untrusted code within AI agent systems.

LLM Metadata is a lightweight static API designed for discovering and integrating LLM metadata. It provides a high-throughput friendly, static-by-default interface that serves static JSON via GitHub Pages. The sources for the metadata include models.dev/api.json and contributions from the basellm community. The tool allows for easy rebuilding on change and offers various scripts for compiling TypeScript, building the API, and managing the project. It also supports internationalization for both documentation and API, enabling users to add new languages and localize capability labels and descriptions. The tool follows an auto-update policy based on a configuration file and allows for directory-based overrides for providers and models, facilitating customization and localization of metadata.

Smithers is a tool for declarative AI workflow orchestration using React components. It allows users to define complex multi-agent workflows as component trees, ensuring composability, durability, and error handling. The tool leverages React's re-rendering mechanism to persist outputs to SQLite, enabling crashed workflows to resume seamlessly. Users can define schemas for task outputs, create workflow instances, define agents, build workflow trees, and run workflows programmatically or via CLI. Smithers supports components for pipeline stages, structured output validation with Zod, MDX prompts, validation loops with Ralph, dynamic branching, and various built-in tools like read, edit, bash, grep, and write. The tool follows a clear workflow execution process involving defining, rendering, executing, re-rendering, and repeating tasks until completion, all while storing task results in SQLite for fault tolerance.

OGPT.nvim is a Neovim plugin that enables users to interact with various language models (LLMs) such as Ollama, OpenAI, TextGenUI, and more. Users can engage in interactive question-and-answer sessions, have persona-based conversations, and execute customizable actions like grammar correction, translation, keyword generation, docstring creation, test addition, code optimization, summarization, bug fixing, code explanation, and code readability analysis. The plugin allows users to define custom actions using a JSON file or plugin configurations.

Webscout is an all-in-one Python toolkit for web search, AI interaction, digital utilities, and more. It provides access to diverse search engines, cutting-edge AI models, temporary communication tools, media utilities, developer helpers, and powerful CLI interfaces through a unified library. With features like comprehensive search leveraging Google and DuckDuckGo, AI powerhouse for accessing various AI models, YouTube toolkit for video and transcript management, GitAPI for GitHub data extraction, Tempmail & Temp Number for privacy, Text-to-Speech conversion, GGUF conversion & quantization, SwiftCLI for CLI interfaces, LitPrinter for styled console output, LitLogger for logging, LitAgent for user agent generation, Text-to-Image generation, Scout for web parsing and crawling, Awesome Prompts for specialized tasks, Weather Toolkit, and AI Search Providers.

WebScout is a versatile tool that allows users to search for anything using Google, DuckDuckGo, and phind.com. It contains AI models, can transcribe YouTube videos, generate temporary email and phone numbers, has TTS support, webai (terminal GPT and open interpreter), and offline LLMs. It also supports features like weather forecasting, YT video downloading, temp mail and number generation, text-to-speech, advanced web searches, and more.

llm.nvim is a plugin for Neovim that enables code completion using LLM models. It supports 'ghost-text' code completion similar to Copilot and allows users to choose their model for code generation via HTTP requests. The plugin interfaces with multiple backends like Hugging Face, Ollama, Open AI, and TGI, providing flexibility in model selection and configuration. Users can customize the behavior of suggestions, tokenization, and model parameters to enhance their coding experience. llm.nvim also includes commands for toggling auto-suggestions and manually requesting suggestions, making it a versatile tool for developers using Neovim.

Nexus is a tool that acts as a unified gateway for multiple LLM providers and MCP servers. It allows users to aggregate, govern, and control their AI stack by connecting multiple servers and providers through a single endpoint. Nexus provides features like MCP Server Aggregation, LLM Provider Routing, Context-Aware Tool Search, Protocol Support, Flexible Configuration, Security features, Rate Limiting, and Docker readiness. It supports tool calling, tool discovery, and error handling for STDIO servers. Nexus also integrates with AI assistants, Cursor, Claude Code, and LangChain for seamless usage.

Ollama is a powerful tool for running large language models locally or on your own infrastructure. It provides a full implementation of the Ollama API, support for streaming requests, and tool use capability. Users can interact with Ollama in Elixir to generate completions, chat messages, and perform streaming requests. The tool also supports function calling on compatible models, allowing users to define tools with clear descriptions and arguments. Ollama is designed to facilitate natural language processing tasks and enhance user interactions with language models.

Mistreevous is a library written in TypeScript for Node and browsers, used to declaratively define, build, and execute behaviour trees for creating complex AI. It allows defining trees with JSON or a minimal DSL, providing in-browser editor and visualizer. The tool offers methods for tree state, stepping, resetting, and getting node details, along with various composite, decorator, leaf nodes, callbacks, guards, and global functions/subtrees. Version history includes updates for node types, callbacks, global functions, and TypeScript conversion.

Gp.nvim (GPT prompt) Neovim AI plugin provides a seamless integration of GPT models into Neovim, offering features like streaming responses, extensibility via hook functions, minimal dependencies, ChatGPT-like sessions, instructable text/code operations, speech-to-text support, and image generation directly within Neovim. The plugin aims to enhance the Neovim experience by leveraging the power of AI models in a user-friendly and native way.

The Universal Tool Calling Protocol (UTCP) is a secure and scalable standard for defining and interacting with tools across various communication protocols. UTCP emphasizes scalability, extensibility, interoperability, and ease of use. It offers a modular core with a plugin-based architecture, making it extensible, testable, and easy to package. The repository contains the complete UTCP Python implementation with core components and protocol-specific plugins for HTTP, CLI, Model Context Protocol, file-based tools, and more.

Terraform Provider for CAST AI is a tool that allows users to manage their CAST AI resources using Terraform. It provides a seamless integration between Terraform and CAST AI platform, enabling users to define and manage their infrastructure as code. The provider supports various features such as setting up cluster configurations, managing node templates, and configuring autoscaler policies. Users can easily install the provider, pass API keys, and leverage the provider's functionalities to automate the deployment and management of their CAST AI resources.

AirGo is a front and rear end separation, multi user, multi protocol proxy service management system, simple and easy to use. It supports vless, vmess, shadowsocks, and hysteria2.

Mosec is a high-performance and flexible model serving framework for building ML model-enabled backend and microservices. It bridges the gap between any machine learning models you just trained and the efficient online service API. * **Highly performant** : web layer and task coordination built with Rust 🦀, which offers blazing speed in addition to efficient CPU utilization powered by async I/O * **Ease of use** : user interface purely in Python 🐍, by which users can serve their models in an ML framework-agnostic manner using the same code as they do for offline testing * **Dynamic batching** : aggregate requests from different users for batched inference and distribute results back * **Pipelined stages** : spawn multiple processes for pipelined stages to handle CPU/GPU/IO mixed workloads * **Cloud friendly** : designed to run in the cloud, with the model warmup, graceful shutdown, and Prometheus monitoring metrics, easily managed by Kubernetes or any container orchestration systems * **Do one thing well** : focus on the online serving part, users can pay attention to the model optimization and business logic

The 'llm-code-interpreter' repository is a deprecated plugin that provides a code interpreter on steroids for ChatGPT by E2B. It gives ChatGPT access to a sandboxed cloud environment with capabilities like running any code, accessing Linux OS, installing programs, using filesystem, running processes, and accessing the internet. The plugin exposes commands to run shell commands, read files, and write files, enabling various possibilities such as running different languages, installing programs, starting servers, deploying websites, and more. It is powered by the E2B API and is designed for agents to freely experiment within a sandboxed environment.

Pezzo is a fully cloud-native and open-source LLMOps platform that allows users to observe and monitor AI operations, troubleshoot issues, save costs and latency, collaborate, manage prompts, and deliver AI changes instantly. It supports various clients for prompt management, observability, and caching. Users can run the full Pezzo stack locally using Docker Compose, with prerequisites including Node.js 18+, Docker, and a GraphQL Language Feature Support VSCode Extension. Contributions are welcome, and the source code is available under the Apache 2.0 License.

Learn Cloud Applied Generative AI Engineering (GenEng) is a course focusing on the application of generative AI technologies in various industries. The course covers topics such as the economic impact of generative AI, the role of developers in adopting and integrating generative AI technologies, and the future trends in generative AI. Students will learn about tools like OpenAI API, LangChain, and Pinecone, and how to build and deploy Large Language Models (LLMs) for different applications. The course also explores the convergence of generative AI with Web 3.0 and its potential implications for decentralized intelligence.

This repository contains shared codebase for two projects: gcloud-aio and gcloud-rest. gcloud-aio is built for Python 3's asyncio, while gcloud-rest is a threadsafe requests-based implementation. It provides clients for Google Cloud services like Auth, BigQuery, Datastore, KMS, PubSub, Storage, and Task Queue. Users can install the library using pip and refer to the documentation for usage details. Developers can contribute to the project by following the contribution guide.

Fluid is an open source Kubernetes-native Distributed Dataset Orchestrator and Accelerator for data-intensive applications, such as big data and AI applications. It implements dataset abstraction, scalable cache runtime, automated data operations, elasticity and scheduling, and is runtime platform agnostic. Key concepts include Dataset and Runtime. Prerequisites include Kubernetes version > 1.16, Golang 1.18+, and Helm 3. The tool offers features like accelerating remote file accessing, machine learning, accelerating PVC, preloading dataset, and on-the-fly dataset cache scaling. Contributions are welcomed, and the project is under the Apache 2.0 license with a vendor-neutral approach.

AIGES is a core component of the Athena Serving Framework, designed as a universal encapsulation tool for AI developers to deploy AI algorithm models and engines quickly. By integrating AIGES, you can deploy AI algorithm models and engines rapidly and host them on the Athena Serving Framework, utilizing supporting auxiliary systems for networking, distribution strategies, data processing, etc. The Athena Serving Framework aims to accelerate the cloud service of AI algorithm models and engines, providing multiple guarantees for cloud service stability through cloud-native architecture. You can efficiently and securely deploy, upgrade, scale, operate, and monitor models and engines without focusing on underlying infrastructure and service-related development, governance, and operations.